The potential of large language models (LLMs) to revolutionize fields ranging from content generation to advanced reasoning is indisputable. However, their occasional tendency to “hallucinate”—generate outputs that conflict with reality—poses significant challenges. Addressing this issue is not merely about mitigating

Continue ReadingMechanistic Interpretability: Paving the Future of LLMs

Mechanistic interpretability is the study of machine learning models to decode and explain their internal decision-making processes. This field has gained prominence with the rise of deep learning and neural networks, which achieve superhuman performance in tasks by learning from

Continue ReadingUnderstanding Mechanistic Interpretability: A Guide to Unraveling the Inner Workings of Machine Learning Models